Path Tracking Control for a Transformable Wheel-Legged Robot Using Model Predictive Control
Chongping Sun, Na Zhao, Kaijie Zhao, Yudong Luo, Yantao Shen
AI summary
Problem
Existing controllers for transformable wheel-legged robots struggle with complex autonomous path tracking due to variable wheel radii, high degrees of freedom, and strong nonlinear coupling, often relying on open-loop or passive mechanisms that lack precision and stability guarantees.
Approach
The authors develop a three-layer kinematic and stability model paired with a two-level controller: a high-level MPC optimizes real-time path tracking errors under constraints, while a low-level feedforward controller translates desired commands into coordinated wheel radius and arm angle adjustments.
Key results
- A complete whole-body kinematic and stability model mapping chassis motion to variable wheel radii and arm angles
- A hierarchical control architecture integrating MPC for pose tracking with feedforward control for actuator coordination
- Quantitative stability constraints ensuring the robot avoids tipping during wheel diameter morphing
- Experimental validation demonstrating accurate path tracking and stable operation across flat and uneven terrains in multiple wheel modes
Why it matters
Enables reliable autonomous navigation for morphing robots in complex environments, advancing the practical deployment of reconfigurable legged-wheeled platforms in logistics, search-and-rescue, and hazardous operations.
Abstract
Transformable wheel-legged robots can adjust their configuration according to terrain conditions, enabling effective operation in harsh environments. While existing con- trollers based on preset commands have successfully demon- strated the feasibility of reconfigurable mechanisms, they still struggle to handle complex autonomous operations. To ad- dress this, we develop a comprehensive motion model for such robots, encompassing chassis kinematics, chassis-wheel kinematics, and stability models, along with a hierarchical path tracking method. The upper controller uses model predictive control with an error state-space model to optimize real-time tracking error under input constraints and generate desired commands. The lower controller utilizes feedforward control to convert desired inputs into actual ones, while accommo- dating physical constraints and geometric coupling associated with variable-radius wheels. Comparative analyses confirm the effectiveness of the proposed approach and demonstrate the robot’s performance under different wheel modes.